1. Field of the Invention
This invention is in the field of extraction of target signatures using a probability distribution measuring geometric properties of the targets under consideration.
2. Description of the Related Art
An important function of a radar system, whether a Real Beam type, Synthetic Aperture (SAR) or Interferometric SAR is to detect a target as well as identify it. Radar target detection and identification have been proven necessary in military surveillance, reconnaissance, and combat missions. The detection and identification of targets provide real-time assessment of the number and the locations of targets of interest.
One method of target detection and identification is to process the image acquired by the radar using, for example, Synthetic Aperture Radar (SAR) technology. By processing a SAR generated image, the features of a target can be extracted and matched to a database for identification.
The general principle behind SAR is to obtain high resolution images by coherently combining the amplitude and phase information of separate radar returns from a plurality of sequentially transmitted pulses from a relatively small antenna on a moving platform. The returns from the plurality of pulses transmitted during a SAR image, when coherently combined and processed, result in image quality comparable to a longer antenna, corresponding approximately to the synthetic “length” traveled by the antenna during the acquisition of the image.
High resolution SAR maps are obtained by coherently combining return signals reflected from transmitted pulses in the cross range direction from radar platform movement. However, formation of focused SAR images or maps requires accurate information on platform position and velocity to shift and focus the received radar returns over the duration of the image acquisition time, the array length, so as to have a useful, phase adjusted combination of pulse returns from multiple pulses transmitted at different times from different radar positions. The process of aligning pulses in time and space for coherent integration is referred to as motion compensation, and is usually performed with the raw radar data, at the early stage of the image formation process.
One aspect of achieving coherent integration of pulses into one SAR image is the need for some form of inertial navigation/ground positioning satellite system (INS/GPS) to indicate the spatial and time coordinates of each transmitted and received (or reflected) pulse. These time and space coordinates of radar returns need to be known to a relatively high accuracy, typically in fractions of a wavelength, to arrive at a clear, focused, unsmeared image. Sometimes the alignment of pulses using the INS/GPS is imperfect, especially towards the edge of the image, introducing “snow” or a grainy character into the SAR image, making it difficult to discern target outline from its background.
It is this grainy character that tends to obfuscate a SAR image thus requiring robust algorithms to extract a target from the SAR image as well as identifying it. The radar image varies from radar to radar depending on the accuracy of the particular INS/GPS, the position of the target within the imaging area, instantaneous operating frequency, as well as glint/fading and target fluctuations. Thus, unlike photographic images, target detection and identification requires a robust approach capable of compensating for characteristics specific to a particular radar system, its operation and type of target being imaged and identified.
Attempts have been made towards target identification extracted from radar images. For example, J. Wissinger, et. al., in MSTAR's Extensible Search Engine and Model-Based Inferencing Toolkit, SPIE 13th Annual International Symposium on AeroSene, Algorithms for SAR Imagery VI, rely on models to implement an algorithm for target identification. During operation, all targets under consideration are forced into one of the known target classes. There is no mechanism to adapt for an unknown target. Thus a high false alarm rate is encountered.
Similarly, J. De Bonet, P. Viola, and J. Fisher, in Flexible Histograms: A Multiresolution Target Discrimination Model, SPIE Proceedings, 1998, rely only on multiscale features of targets. Again, this yields a relatively high false alarm rate.
Automatic target recognizers (ATR), typical of the type described by Wissinger above, have a tendency to erroneously place unknown objects, such as a bulldozer, into one of the known military target classifications. This results in a high false alarm rate. Thus, it is desired to develop a method for rejecting unknown targets especially in high resolution (1 ft) imagery.
Because of above limitations of the prior art high false alarm rates are encountered thus limiting the utility of an imaging and target detection radar.